imgaug image enhancement record

 

Man of few words said, first served imgaug official website: https://imgaug.readthedocs.io/en/latest/index.html

Contents official website, where you can know what it can do to enhance the scope of:

Install a .imgaug

Dependent libraries

pip install six numpy scipy matplotlib scikit-image opencv-python imageio

Then, directly installed in the terminal, may be mounted on Prompt with anaconda:

pip install imgaug

Here, if unfortunate, everything seems to be in a smooth installation process, a series of sudden red error, as follows

G:\python\Pyhton36\Scripts>pip install imgaug
Collecting imgaug
  Downloading https://files.pythonhosted.org/packages/af/fc/c56a7da8c23122b7c5325b941850013880a7a93c21dc95e2b1ecd4750108/imgaug-0.2.7-py3-none-any.whl (644kB)
    100% |████████████████████████████████| 645kB 73kB/s
Requirement already satisfied: scikit-image>=0.11.0 in g:\python\pyhton36\lib\site-packages (from imgaug) (0.14.1)
Collecting imageio (from imgaug)
  Downloading https://files.pythonhosted.org/packages/28/b4/cbb592964dfd71a9de6a5b08f882fd334fb99ae09ddc82081dbb2f718c81/imageio-2.4.1.tar.gz (3.3MB)
    100% |████████████████████████████████| 3.3MB 438kB/s
Collecting Shapely (from imgaug)
  Downloading https://files.pythonhosted.org/packages/a2/fb/7a7af9ef7a35d16fa23b127abee272cfc483ca89029b73e92e93cdf36e6b/Shapely-1.6.4.post2.tar.gz (225kB)
    100% |████████████████████████████████| 235kB 181kB/s
    Complete output from command python setup.py egg_info:
    Traceback (most recent call last):
      File "<string>", line 1, in <module>
      File "C:\Users\34905\AppData\Local\Temp\pip-install-43zide7u\Shapely\setup.py", line 80, in <module>
        from shapely._buildcfg import geos_version_string, geos_version, \
      File "C:\Users\34905\AppData\Local\Temp\pip-install-43zide7u\Shapely\shapely\_buildcfg.py", line 200, in <module>
        lgeos = CDLL("geos_c.dll")
      File "g:\python\pyhton36\lib\ctypes\__init__.py", line 348, in __init__
        self._handle = _dlopen(self._name, mode)
    OSError: [WinError 126] 找不到指定的模块。
 
    ----------------------------------------
Command "python setup.py egg_info" failed with error code 1 in C:\Users\34905\AppData\Local\Temp\pip-install-43zide7u\Shapely\

Check out the predecessors of information online, you need to install shapely

Through this web site  https://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely  (this is a very good expansion pack unofficial website, there are many tools available) to find their own version of python the shapely downloaded to the specified path

I am a windows, 64-bit operating system, python3.5, I chose the following

Then install the following command:

G:\python\Pyhton36\Scripts>python -m pip install Shapely‑1.6.4.post2‑cp35‑cp35m‑win_amd64.whl

Finally, repeat the command before installing imgaug:

pip install imgaug

Get, call it a day, the installation is complete

Reference: https://blog.csdn.net/qq_16065939/article/details/85080630

---

Two .github source code to meet the problem and enhance XML file with BoundingBox batch

github original author address: https://github.com/xinyu-ch/Data-Augment

Diudiu there is a mistake here was modified to look down (Section III), has been debugging, available at some small problems met here speak

1. If, unfortunately, met the following problem, which is a common problem, more unified solutions: https://blog.csdn.net/qq_38163755/article/details/84494796

Resolve as follows:

A first solution, increasing = encoding 'UTF-. 8' :

FILE_OBJECT= open( 'train.txt','r', encoding='UTF-8' )

The second method, binary read :

FILE_OBJECT= open( 'train.txt', 'rb' )

After that, you can look at the third quarter, we are doing a modification of the above github part of the commissioning available

---

III. BoundingBox with enhanced XML file batch debugging (can be directly used)

1. Xml file and image files created after the folder you want to save enhancements
2. Set up in advance what way enhanced image is rotated, or add noise, it is to enhance the brightness, or supplementary translation
3. Then came a picture is read, and the coordinates of the image information in the corresponding xml tag
4. And a rectangular frame image transform
5. After the last saved enhance images and xml files

import xml.etree.ElementTree as ET
import pickle
import os
from os import getcwd
import numpy as np
from PIL import Image
import shutil
import matplotlib.pyplot as plt

import imgaug as ia
from imgaug import augmenters as iaa

ia.seed(1)

def read_xml_annotation(root, image_id):
    in_file = open(os.path.join(root, image_id), encoding='UTF-8')
    tree = ET.parse(in_file)
    root = tree.getroot()
    bndboxlist = []

    for object in root.findall('object'):  # 找到root节点下的所有country节点
        bndbox = object.find('bndbox')  # 子节点下节点rank的值

        xmin = int(bndbox.find('xmin').text)
        xmax = int(bndbox.find('xmax').text)
        ymin = int(bndbox.find('ymin').text)
        ymax = int(bndbox.find('ymax').text)
        # print(xmin,ymin,xmax,ymax)
        bndboxlist.append([xmin, ymin, xmax, ymax])
        # print(bndboxlist)

    # ndbox = root.find('object').find('bndbox')
    return bndboxlist


# (506.0000, 330.0000, 528.0000, 348.0000) -> (520.4747, 381.5080, 540.5596, 398.6603)
def change_xml_annotation(root, image_id, new_target):
    new_xmin = new_target[0]
    new_ymin = new_target[1]
    new_xmax = new_target[2]
    new_ymax = new_target[3]

    in_file = open(os.path.join(root, str(image_id) + '.xml'), encoding='UTF-8' )  # 这里root分别由两个意思
    tree = ET.parse(in_file)
    xmlroot = tree.getroot()
    object = xmlroot.find('object')
    bndbox = object.find('bndbox')
    xmin = bndbox.find('xmin')
    xmin.text = str(new_xmin)
    ymin = bndbox.find('ymin')
    ymin.text = str(new_ymin)
    xmax = bndbox.find('xmax')
    xmax.text = str(new_xmax)
    ymax = bndbox.find('ymax')
    ymax.text = str(new_ymax)
    tree.write(os.path.join(root, str("%06d" % (str(id) + '.xml'))))


def change_xml_list_annotation(root, image_id, new_target, saveroot, id):
    in_file = open(os.path.join(root, str(image_id) + '.xml'), encoding='UTF-8' )  # 这里root分别由两个意思
    tree = ET.parse(in_file)
    elem = tree.find('filename')
    elem.text = (str("%06d" % int(id)) + '.png')
    xmlroot = tree.getroot()
    index = 0

    for object in xmlroot.findall('object'):  # 找到root节点下的所有country节点
        bndbox = object.find('bndbox')  # 子节点下节点rank的值

        # xmin = int(bndbox.find('xmin').text)
        # xmax = int(bndbox.find('xmax').text)
        # ymin = int(bndbox.find('ymin').text)
        # ymax = int(bndbox.find('ymax').text)

        new_xmin = new_target[index][0]
        new_ymin = new_target[index][1]
        new_xmax = new_target[index][2]
        new_ymax = new_target[index][3]

        xmin = bndbox.find('xmin')
        xmin.text = str(new_xmin)
        ymin = bndbox.find('ymin')
        ymin.text = str(new_ymin)
        xmax = bndbox.find('xmax')
        xmax.text = str(new_xmax)
        ymax = bndbox.find('ymax')
        ymax.text = str(new_ymax)

        index += 1

        print("index=",index)


    tree.write(os.path.join(saveroot, str("%06d" % int(id)) + '.xml'))


def mkdir(path):
    # 去除首位空格
    path = path.strip()
    # 去除尾部 \ 符号
    path = path.rstrip("\\")
    # 判断路径是否存在
    # 存在     True
    # 不存在   False
    isExists = os.path.exists(path)
    # 判断结果
    if not isExists:
        # 如果不存在则创建目录
        # 创建目录操作函数
        os.makedirs(path)
        print(path + ' 创建成功')
        return True
    else:
        # 如果目录存在则不创建，并提示目录已存在
        print(path + ' 目录已存在')
        return False


if __name__ == "__main__":

    IMG_DIR = "F:\\image\\raw_xml"
    XML_DIR = "F:\\image\\xml"

    AUG_XML_DIR = "./Annotations"  # 存储增强后的XML文件夹路径
    try:
        shutil.rmtree(AUG_XML_DIR)
    except FileNotFoundError as e:
        a = 1
    mkdir(AUG_XML_DIR)

    AUG_IMG_DIR = "./JPEGImages"  # 存储增强后的影像文件夹路径
    try:
        shutil.rmtree(AUG_IMG_DIR)
    except FileNotFoundError as e:
        a = 1
    mkdir(AUG_IMG_DIR)

    AUGLOOP = 2  # 每张影像增强的数量

    boxes_img_aug_list = []
    new_bndbox = []
    new_bndbox_list = []

    # 影像增强
    seq = iaa.Sequential([
        iaa.Flipud(0.5),  # vertically flip 20% of all images
        iaa.Fliplr(0.5),  # 镜像
        iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)),
        iaa.Crop(px=(0, 16)),
        iaa.Add((-10, 10), per_channel=0.5),
        iaa.Multiply((1.2, 1.5)),  # change brightness, doesn't affect BBs
        iaa.Affine(
            translate_px={"x": 15, "y": 15},
            scale=(0.8, 0.95)
            #rotate=(-30, 30)
        )  # translate by 40/60px on x/y axis, and scale to 50-70%, affects BBs
    ])

    for root, sub_folders, files in os.walk(XML_DIR):

        for name in files:

            bndbox = read_xml_annotation(XML_DIR, name)
            shutil.copy(os.path.join(XML_DIR, name), AUG_XML_DIR)
            shutil.copy(os.path.join(IMG_DIR, name[:-4] + '.png'), AUG_IMG_DIR)
            print(os.path.join(IMG_DIR, name[:-4] + '.png'))

            for epoch in range(AUGLOOP):
                seq_det = seq.to_deterministic()  # 保持坐标和图像同步改变，而不是随机
                # 读取图片
                img = Image.open(os.path.join(IMG_DIR, name[:-4] + '.png'))
                # sp = img.size
                img = np.asarray(img)
                # bndbox 坐标增强
                for i in range(len(bndbox)):
                    bbs = ia.BoundingBoxesOnImage([
                        ia.BoundingBox(x1=bndbox[i][0], y1=bndbox[i][1], x2=bndbox[i][2], y2=bndbox[i][3]),
                    ], shape=img.shape)

                    bbs_aug = seq_det.augment_bounding_boxes([bbs])[0]
                    boxes_img_aug_list.append(bbs_aug)

                    # new_bndbox_list:[[x1,y1,x2,y2],...[],[]]
                    n_x1 = int(max(1, min(img.shape[1], bbs_aug.bounding_boxes[0].x1)))
                    n_y1 = int(max(1, min(img.shape[0], bbs_aug.bounding_boxes[0].y1)))
                    n_x2 = int(max(1, min(img.shape[1], bbs_aug.bounding_boxes[0].x2)))
                    n_y2 = int(max(1, min(img.shape[0], bbs_aug.bounding_boxes[0].y2)))
                    if n_x1 == 1 and n_x1 == n_x2:
                        n_x2 += 1
                    if n_y1 == 1 and n_y2 == n_y1:
                        n_y2 += 1
                    if n_x1 >= n_x2 or n_y1 >= n_y2:
                        print('error', name)
                    new_bndbox_list.append([n_x1, n_y1, n_x2, n_y2])

                    # 存储变化后的图片
                    image_aug = seq_det.augment_images([img])[0]
                    path = os.path.join(AUG_IMG_DIR,str("%06d" % (len(files) + int(name[:-4]) + epoch * 250)) + '.png')

                    image_auged = bbs.draw_on_image(image_aug, thickness=0)####################################

                    Image.fromarray(image_auged).save(path)

                # 存储变化后的XML
                change_xml_list_annotation(XML_DIR, name[:-4], new_bndbox_list, AUG_XML_DIR,len(files) + int(name[:-4]) + epoch * 250)
                print(str("%06d" % (len(files) + int(name[:-4]) + epoch * 250)) + '.png')
                new_bndbox_list = []

 Dead code reading method for writing data: elem.text = (str ( "% 06d"% int (id)) + '.png')

If not necessary, to be named in this form as follows

After enhancement, the results with labelImg view as follows:

Details portion reference herein: https://blog.csdn.net/coooo0l/article/details/84492916#commentsedit